Atrial fibrillation (AF) is the most common clinically significant arrhythmia, often severely disrupting cardiac hemodynamics and drastically increasing the risk of thromboembolic events. Around 90% of such intracardiac thrombus formation in AF patients takes place in the left atrial appendage (LAA). Such thrombus have been related to blood stasis, which at the moment, can be only assessed through noisy imaging data from transesophageal echocardiography (TEE) at one single point in space and time, vastly oversimplifying the characterization of the complex 4D nature of blood flow patterns. Alternatively, attempts have been made to relate LAA morphology to the risk of thrombi formation, some studies suggesting reduced risk of thrombosis on chicken-wing morphologies. Nonetheless, such classification of the LAA morphology has been found to be highly inconsistent and subjective, excluding as well, several fundamental morphological parameters such as the ostium size or the pulmonary vein (PV) orientation among others. More recently, computational fluid dynamics (CFD) have been employed on the left atrium (LA), seeking to assess the risk of thrombogenesis more quantitatively. CFD has proven to be an invaluable tool in establishing a mechanistic relation between patient-specific organ morphology and its characteristic hemodynamics. In fact, it has long been implemented in other human tissues, such as the coronary arteries, cerebral aneurysms and the aorta with unparalleled success, enabling early diagnosis and risk assessment of various cardiovascular diseases. Nevertheless, traditional CFD methods are renowned for their large memory requirements and long computing times, which severely hinders its suitability for time-sensitive clinical applications. Hence, this thesis seeks to harness the immense potential of deep learning (DL) by developing a deep neural network (DNN), with the objective of generating a fast and accurate surrogate of CFD, capable of instantaneously evaluating the risk of thrombus formation in the LAA. Already having revolutionized fields such as data processing, it has only recently begun to employ DNNs in high-dimensional, complex dynamical systems such as fluid dynamics. In fact to our knowledge, this study represents the first successful implementation of a DL surrogate of CFD analysis in a structure as complex as the LAA, which had only been previously attempted in the aorta. For that purpose, two DL architectures have been successfully designed and trained, which receive the specific LAA geometry as an input, and accurately predict its corresponding endothelial cell activation potential (ECAP) map, parameter linked to the risk of thrombosis. The first approach, is based on a simple fully-connected feedforward network, while the latter, also embeds unsupervised learning. An statistical shape model (SSM) of the LAA was created to generate the training dataset, encompassing 210 virtual shapes, on which CFD simulations were performed to attain the ground truth ECAP mappings. Once trained, the final D...
Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived from CFD simulations, solely from the patient-specific LAA morphology. To this end, a set of popular DL approaches were evaluated, including fully connected networks (FCN), convolutional neural networks (CNN), and geometric deep learning. While the latter directly operated over non-Euclidean domains, the FCN and CNN approaches required previous registration or 2D mapping of the input LAA mesh. First, the superior performance of the graph-based DL model was demonstrated in a dataset consisting of 256 synthetic and real LAA, where CFD simulations with simplified boundary conditions were run. Subsequently, the adaptability of the geometric DL model was further proven in a more realistic dataset of 114 cases, which included the complete patient-specific LA and CFD simulations with more complex boundary conditions. The resulting DL framework successfully predicted the overall distribution of the ECAP in both datasets, based solely on anatomical features, while reducing computational times by orders of magnitude compared to conventional CFD solvers.
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